风电机组发电机轴承电腐蚀故障的分析诊断

Diagnosis of Electrical Corrosion Fault in Wind Turbine Generator Bearing Based on Vibration Signal Analysis

电腐蚀故障是风电机组发电机轴承的常见故障模式,电腐蚀故障通常分布在整个轴承滚道上,产生的振动响应信号中故障冲击特征往往不如局部故障明显,因此容易被忽视。针对电腐蚀故障振动信号的这种特点,采用一种最小熵解卷积方法对振动信号进行预处理,增强信号中的故障冲击成分。然后再应用包络谱分析方法提取故障特征信息,以提升故障诊断的效果。论述了最小熵解卷积方法的基本原理和实现流程,将该方法应用于一台实际风电机组发电机轴承的电腐蚀故障诊断中,通过对实测振动信号的分析处理,实现了电腐蚀故障的识别诊断,验证了最小熵解卷积方法对故障信息增强的使用效果。

The electrical corrosion is a normal fault mode of generator bearing in wind turbine.Besides,electrical corrosion fault has ripped effect on bearing surface and excite relative weaker impulse response than local fault,which makes it difficult to extract fault features.Based on the characteristics of vibration signal due to the electrical corrosion,the minimum entropy deconvolution(MED)is used as the pre-processing method to enhance impulse fault feature in vibration signals.Then,the envelope spectrum is used to identify bearing fault feature to improve the diagnosis effect.In this paper,the basic principle and implementation process of MED is introduced.Furthermore,the proposed method is used to diagnosis the electrical corrosion fault of a real case of bearing in a wind turbine generator.Vibration signal measured on the generator bearing box is processed with MED and envelope spectrum.And diagnosis of electrical corrosion faults is realized.The results confirm validity of the application effect of minimum entropy deconvolution method.